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BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding Neural Network/Molecular Mechanics Simulations

[Image: see text] Hybrid quantum mechanics/molecular mechanics (QM/MM) simulations have advanced the field of computational chemistry tremendously. However, they require the partitioning of a system into two different regions that are treated at different levels of theory, which can cause artifacts...

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Autores principales: Lier, Bettina, Poliak, Peter, Marquetand, Philipp, Westermayr, Julia, Oostenbrink, Chris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9082612/
https://www.ncbi.nlm.nih.gov/pubmed/35467875
http://dx.doi.org/10.1021/acs.jpclett.2c00654
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author Lier, Bettina
Poliak, Peter
Marquetand, Philipp
Westermayr, Julia
Oostenbrink, Chris
author_facet Lier, Bettina
Poliak, Peter
Marquetand, Philipp
Westermayr, Julia
Oostenbrink, Chris
author_sort Lier, Bettina
collection PubMed
description [Image: see text] Hybrid quantum mechanics/molecular mechanics (QM/MM) simulations have advanced the field of computational chemistry tremendously. However, they require the partitioning of a system into two different regions that are treated at different levels of theory, which can cause artifacts at the interface. Furthermore, they are still limited by high computational costs of quantum chemical calculations. In this work, we develop the buffer region neural network (BuRNN), an alternative approach to existing QM/MM schemes, which introduces a buffer region that experiences full electronic polarization by the inner QM region to minimize artifacts. The interactions between the QM and the buffer region are described by deep neural networks (NNs), which leads to the high computational efficiency of this hybrid NN/MM scheme while retaining quantum chemical accuracy. We demonstrate the BuRNN approach by performing NN/MM simulations of the hexa-aqua iron complex.
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spelling pubmed-90826122022-05-10 BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding Neural Network/Molecular Mechanics Simulations Lier, Bettina Poliak, Peter Marquetand, Philipp Westermayr, Julia Oostenbrink, Chris J Phys Chem Lett [Image: see text] Hybrid quantum mechanics/molecular mechanics (QM/MM) simulations have advanced the field of computational chemistry tremendously. However, they require the partitioning of a system into two different regions that are treated at different levels of theory, which can cause artifacts at the interface. Furthermore, they are still limited by high computational costs of quantum chemical calculations. In this work, we develop the buffer region neural network (BuRNN), an alternative approach to existing QM/MM schemes, which introduces a buffer region that experiences full electronic polarization by the inner QM region to minimize artifacts. The interactions between the QM and the buffer region are described by deep neural networks (NNs), which leads to the high computational efficiency of this hybrid NN/MM scheme while retaining quantum chemical accuracy. We demonstrate the BuRNN approach by performing NN/MM simulations of the hexa-aqua iron complex. American Chemical Society 2022-04-25 2022-05-05 /pmc/articles/PMC9082612/ /pubmed/35467875 http://dx.doi.org/10.1021/acs.jpclett.2c00654 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Lier, Bettina
Poliak, Peter
Marquetand, Philipp
Westermayr, Julia
Oostenbrink, Chris
BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding Neural Network/Molecular Mechanics Simulations
title BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding Neural Network/Molecular Mechanics Simulations
title_full BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding Neural Network/Molecular Mechanics Simulations
title_fullStr BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding Neural Network/Molecular Mechanics Simulations
title_full_unstemmed BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding Neural Network/Molecular Mechanics Simulations
title_short BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding Neural Network/Molecular Mechanics Simulations
title_sort burnn: buffer region neural network approach for polarizable-embedding neural network/molecular mechanics simulations
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9082612/
https://www.ncbi.nlm.nih.gov/pubmed/35467875
http://dx.doi.org/10.1021/acs.jpclett.2c00654
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